Image reconstruction system and method

ABSTRACT

A method and system for image reconstruction are provided. A projection image of a projection object may be obtained. A processed projection image may be generated based on the projection image through one or more pre-process operations. A reconstructed image including an artifact may be reconstructed based on the processed projection image. The artifact may be a detector edge artifact, a projection object edge artifact, and a serrated artifact. The detector edge artifact, the projection object edge artifact, and the serrated artifact may be removed from the reconstructed image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of U.S. application Ser. No.15/317,382, filed on Dec. 8, 2016, which is a U.S. national stage entryof International Application No. PCT/CN2016/099061, filed on Sep. 14,2016, which in turn claims priority to Chinese Patent Application No.201510583366.0 filed on Sep. 15, 2015, Chinese Patent Application No.201510583397.6 filed on Sep. 15, 2015, and Chinese Patent ApplicationNo. 201610066684.4 filed on Jan. 29, 2016, the entire contents of eachof which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and moreparticularly, to a system and method for image reconstruction.

BACKGROUND

Imaging reconstruction techniques are widely used in disease diagnosis.However, reconstructed images may include a variety of artifacts, whichmay cause misdiagnose. Thus, it may be desirable to develop an imagereconstruction method and system that may remove or reduce artifacts toimprove the quality of reconstructed image.

SUMMARY

The present disclosure relates to image processing. One aspect of thepresent disclosure relates to a method for image reconstruction. Themethod may include one or more of the following operations. A projectionimage of a projection object may be obtained. A processed projectionimage may be generated according to one or more pre-processingoperations on the projection image. For example, the pre-processing theprojection image may include segmenting the projection image to generatea segmented projection image. A reconstructed image including anartifact may be generated based on the processed projection image. Theartifact may be removed in the reconstructed image.

In some embodiments, the pre-processing the projection image may furtherinclude generating a negative film of the segmented projection image,and correcting a geometrical error of the negative film of the segmentedprojection image.

In some embodiments, the reconstructing the processed projection imageto generate the reconstructed image may include filtering the processedprojection image to generate a filtered projection image including ahighlighted artifact and an X-ray attenuation artifact, correcting thehighlighted artifact and the X-ray attenuation artifact in the filteredprojection image to generate a first image, and performing backprojection to generate the reconstructed image based on the first image.

In some embodiments, the artifact may include a detector edge artifactrelating to a detector edge, a projection object edge artifact relatingto a projection object edge, and a serrated artifact.

In some embodiments, the detector edge artifact, the projection objectedge artifact, and the serrated artifact may be removed in thereconstructed image.

In some embodiments, the reconstructed image may include a tomographicimage.

In some embodiments, the removing serrated artifact in a tomographicimage may include one or more of the following operations. A mappingposition of the detector edge in the tomographic image may bedetermined. A projection object edge in the tomographic image may bedetermined. An intersection point corresponding to the projection objectedge and the mapping position of the detector edge may be determined.Dislocation information of the intersection point based on theintersection point and the serrated artifact may be determined. Theserrated artifact may be removed based on the intersection point and thedislocation information of the intersection point.

In some embodiments, the determining a mapping position of the detectoredge in the tomographic image may include one or more of the followingoperations. A first geometric position relationship between a radiationsource and the detector may be determined. A second geometric positionrelationship between the projection image and the tomographic image maybe determined. Mapping coordinates of pixels in the projection imagebased on the first geometric position relationship and the secondgeometric position relationship may be determined. The mapping positionof the detector edge based on the mapping coordinates of pixels in theprojection image and an imaging area of the detector in projection imagemay be determined.

In some embodiments, the dislocation information of the intersectionpoint is a horizontal distance between the intersection point and apoint on an edge of the serrated artifact.

In some embodiments, the removing the serrated artifact based on theintersection point and the dislocation information of the intersectionpoint may include one or more of the following operations. A projectionobject template of the tomographic image may be created. The serratedartifact may be removed in the projection object template to obtain acorrected projection object template. The serrated artifact may beremoved in the tomographic image based on the corrected projectionobject template.

In some embodiments, the segmenting the projection image to generate asegmented projection image may include one or more of the followingoperations. An average gray value of one or more pixels of theprojection image may be determined. For each pixel of the one or morepixels of the projection image, mark A or mark B may be assigned to thepixel based on a relationship between a gray value of the pixel and theaverage gray value. A boundary of a region of interest based on theassigned mark of each pixel of the one or more pixels of the projectionimage may be determined.

In some embodiments, the boundary of the region of interest may bedetermined based on a seed filling algorithm.

Another aspect of the present disclosure relates to a non-transitorycomputer readable medium including executable instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to effectuate a method for image reconstruction. Insome embodiments, the non-transitory computer readable medium mayinclude instructions for causing a computer to implement the methoddescribed herein.

A further aspect of the present disclosure relates to a system for imagereconstruction. The system may include a pre-procession module topre-process a projection image to generate a processed projection image.In some embodiments, the pre-procession module may include asegmentation unit, a negative film unit, and a geometrical errorcorrection unit. The system may further include a reconstruction moduleto reconstruct the processed projection image to generate areconstructed image including an artifact. In some embodiments, theartifact may be a detector edge artifact relating to a detector edge, aprojection object edge artifact relating to a projection object edge,and a serrated artifact. The system may further include an artifactremoval module to remove the artifact.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 illustrates a schematic diagram of an image reconstruction system100 according to some embodiments of the present disclosure;

FIG. 2A illustrates an exemplary imaging device according to someembodiments of the present disclosure;

FIG. 2B illustrates an architecture of a computer which may be used toimplement a specialized system incorporating the present teaching;

FIG. 3A illustrates an exemplary image processing device according tosome embodiments of the present disclosure;

FIG. 3B is a block diagram illustrating an exemplary pre-processionmodule according to some embodiments of the present disclosure;

FIG. 3C is a block diagram illustrating an exemplary reconstructionmodule according to some embodiments of the present disclosure;

FIG. 3D is a block diagram illustrating an exemplary artifact removalmodule according to some embodiments of the present disclosure;

FIG. 4 illustrates a flowchart illustrating an exemplary process forimage reconstruction in accordance with some embodiments of the presentdisclosure;

FIG. 5 is a flowchart illustrating an exemplary process forpre-processing projection image in accordance with some embodiments ofthe present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for segmentingprojection image in accordance with some embodiments of the presentdisclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determiningthe boundary of a region of interest in accordance with some embodimentsof the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generating areconstructed image in accordance with some embodiments of the presentdisclosure;

FIG. 9 is a flowchart illustrating an exemplary process for removingartifact in a reconstructed image in accordance with some embodiments ofthe present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for removingserrated artifact in accordance with some embodiments of the presentdisclosure;

FIG. 11 is a flowchart illustrating an exemplary process for removingserrated artifact in accordance with some embodiments of the presentdisclosure;

FIG. 12 illustrates a reconstructed image of a mammary gland;

FIG. 13 illustrates an exemplary reconstructed image of a mammary gland;

FIG. 14 illustrates an exemplary reconstructed image of a mammary glandwithout serrated artifact correction;

FIG. 15A and FIG. 15B illustrate exemplary reconstructed images of amammary gland with serrated artifacts;

FIG. 16A to FIG. 16D illustrate exemplary mammary gland templates;

FIG. 17 illustrates exemplary mammary gland reconstructed images;

FIG. 18 illustrates an exemplary projection image of a mammary gland;

FIG. 19 illustrates a process for generating a segmented region bymerging a plurality of regions of interest according to some embodimentsof the present disclosure;

FIG. 20 illustrates a process for generating a segmented region based ona rectangular segmenting algorithm according to some embodiments of thepresent disclosure; and

FIG. 21 illustrates an exemplary reconstructed image of a mammary gland.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

The present disclosure provided herein relates to an imagereconstruction system. Specifically, the present disclosure relates to asystem and method for reconstructing image. According to someembodiments of the present disclosure, the method may includepre-processing a projection image to generate a processed projectionimage. The pre-processing a projection image may include segmenting theprojection image to generate a segmented projection image. The processedprojection image may be reconstructed to generate a reconstructed imageincluding an artifact. The method may further include removing theartifact in the reconstructed image. The removing the artifact in thereconstructed image including removing a detector edge artifact,removing a projection object edge artifact, and removing a serratedartifact.

FIG. 1 illustrates a schematic diagram of an image reconstruction system100 according to some embodiments of the present disclosure. Imagereconstruction system 100 may include an imaging device 110, an imageprocessing device 120, a terminal 130, a display 140, a database 150,and a network 160. In some embodiments, at least part of imageprocessing device 120 may be implemented on computer 200 shown in FIG.2B.

Imaging device 110 may obtain an image. The image may be athree-dimensional (3D) image, a two-dimensional (2D) image, or the like,or a combination thereof. The image may be a projection image, are-projection image, or the like, or a combination thereof. The imagemay be a digital breast tomosynthesis (DBT) image, a full-field digitalmammography system (FFDM) image, and a magnetic resonance (MR) image, orthe like, or a combination thereof. The image may be an image of anobject. For example, the image may be a 3D projection image of a mammarygland. The image may be a 2D projection image of a mammary gland. Insome embodiments, a 3D image may correspond to a stack of 2D images. A2D image may be referred to as a tomographic image or a slice image. Forinstance, a 3D digital image of a mammary gland may correspond to astack of 2D tomographic images of the mammary gland.

Imaging device 110 may utilize various imaging techniques. The imagingtechnique may be a non-invasive imaging technique or an invasive imagingtechnique. The technique may be based on or relate to radiography (e.g.,fluoroscopy, projection radiography, etc.), magnetic resonance imaging(MRI), nuclear medicine (e.g., scintigraphy, single-photon emissioncomputerized tomography (SPECT), positron emission tomography (PET),etc.), ultrasound (e.g., ultrasound scanning (US), etc.), elastography(e.g., quasistatic elastography/strain imaging, shear wave elasticityimaging (SWEI), acoustic radiation force impulse imaging (ARFI),supersonic shear imaging (SSI), and transient elastography, etc.),tactile imaging, photoacoustic imaging, thermography, tomography,conventional tomography, computer-assisted tomography (e.g., X-raycomputed tomography (CT), positron emission tomography (PET), magneticresonance imaging (MRI), etc.), echocardiography, functionalnear-infrared spectroscopy (FNIR), digital subtraction angiography(DSA), computed tomography angiography (CTA), digital radiation (DR),magnetic resonance angiography (MRA), or the like, or a combinationthereof.

In some embodiments, imaging device 110 may include an X-radiationsource and a radiation detector (not shown in FIG. 1). Imaging device110 may use a low dose X-ray to create a three-dimensional image of thebreast. For example, imaging device 110 may be a digital breasttomosynthesis (DBT) shown in FIG. 2A.

Image processing device 120 may process an image. For example, imageprocessing device 120 may reconstruct an image to generate areconstructed image, enhance an image to generate an enhanced image,extract some information from an image, remove artifact of an image, orthe like, or a combination thereof. The image may be obtained by imagingdevice 110 or retrieved from another source (e.g., database 150, astorage, etc.). The reconstructed image may include one or moretomographic images. For example, image processing device 120 mayreconstruct a 3D tomographic image of a mammary gland based on one ormore mammary gland projection images obtained by imaging device 110.

Image processing device 120 may be any kind of device that may processan image. For example, image processing device 120 may include ahigh-performance computer specialized in image processing or transactionprocessing, a personal computer, a portable device, a server, amicroprocessor, an integrated chip, a digital signal processor (DSP), apad, a PDA, or the like, or a combination thereof. In some embodiments,imaging processing device 120 may be implemented on computer 200 shownin FIG. 2B.

Image processing may include performing one or more operations on theimage. The operations may include image manipulation (e.g., rotating,flipping, resizing, cropping, etc.), image segmentation, imagereconstruction, image filtering, image binarization, image overlapping,image matching, image negative film, image artifact removing, colorcorrection, geometric transformation, image noise reduction, imageenhancement, image compression, or the like, or a combination thereof.In some embodiments, image processing device 120 may segment image toget a region of interest, and perform image reconstruction operation onthe region of interest.

Methods used in image processing may include an image reconstructionmethod, an image segmentation method, an image binarization method, animage artifact removing method, or the like, or a combine thereof. Asused herein, “removing” artifact may refer to completely or partiallyremove artifact that is present or identified by an image processingtechnology or method.

Image reconstruction methods may include filtered back projection (FBP),the simultaneous iterative reconstruction technique (SIRT), matrixinversion tomosynthesis (MITS), iterative maximum a posterioristatistical reconstruction, Bayesian-based interactive reconstruction,or the like, or a combination thereof. More descriptions regarding afiltered back projection may be found elsewhere in the presentdisclosure. See, for example, FIG. 4 and FIG. 8, and the descriptionthereof.

Image segmentation methods may include an edge detecting method, athreshold segmenting method, a histogram-based method, a clusteringmethod, a compression-based method, a region-growing method, a graphpartitioning method, or the like, or a combination thereof. More detailsdescriptions regarding region-growing method may be found elsewhere inthe present disclosure. See, for example, FIG. 6 and FIG. 7, and thedescription thereof.

Image artifact removing methods may include a polynomial interpolationmethod, an iterative deblurring method, an expectation-maximizationmethod, an algebraic reconstruction technique, a Markov random fieldmethod, a wavelet method, an ordered subsets convex iterative method, abeam-stop technique, a scanning lead-strip technique, or the like, or acombination thereof. More details descriptions regarding image artifactremoving methods may be found elsewhere in the present disclosure. See,for example, FIG. 10 and FIG. 11, and the description thereof.

Terminal 130 may be connected to or communicate with image processingdevice 120 and allow one or more operators to control the productionand/or display of images on display 140. Terminal 130 may include aninput device, an output device, a control panel (not shown in figure),or the like, or a combination thereof. The input device may be akeyboard, a touch screen, a mouse, a remote controller, a wearabledevice, or the like, or a combination thereof. An input device mayinclude alphanumeric and other keys that may be inputted via a keyboard,a touch screen (e.g., with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be communicated to image processing device 120 vianetwork 160 for further processing. Another type of the input device mayinclude a cursor control device, such as a mouse, a trackball, or cursordirection keys to communicate direction information and commandselections to, for example, image processing device 120 and to controlcursor movement on display 140 or another display device.

Display 140 may display information. The information may include animage before and/or after image processing, a request for input orparameter relating to image acquisition and/or processing, or the like,or a combination thereof. The display device may include a liquidcrystal display (LCD), a light emitting diode (LED)-based display, aflat panel display or curved screen (or television), a cathode ray tube(CRT), or the like, or a combination thereof.

Database 150 may store images and/or relevant information or parameters.Exemplary parameters may include the coordinate of the radiation source,the radiation angle of the radiation source, the coordinate of therotating center of the radiation source, the pixel size of a projectionimage, the width of a projection image, the height of a projectionimage, the coordinate vector of a pixel in a projection image, the widthof a reconstructed image, the height of a reconstructed image, the pixelsize of a reconstructed image, the coordinate vector of a pixel in areconstructed image, or the like, or a combination thereof.

Network 160 may establish connection between different units in system100. Network 160 may be a single network, or a combination of variousnetworks. Network 160 may be a wired network or a wireless network. Thewired network may include using a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or a combination thereof. The wireless network may be aBluetooth, a Near Field Communication (NFC), a wireless local areanetwork (WLAN), WiFi, a Wireless Wide Area Network (WWAN), or the like,or a combination thereof.

It should be noted that the descriptions above in relation to imagereconstruction system 100 is provided for the purposes of illustration,and not intended to limit the scope of the present disclosure. Forpersons having ordinary skills in the art, various variations andmodifications may be conducted under the guidance of the presentdisclosure. However, those variations and modifications do not departthe scope of the present disclosure. For example, part or all of theimage obtained by imaging device 110 may be processed by terminal 130.In some embodiments, imaging device 110 may pre-process the obtainedimage, before the image data is sent to the image processing device 120for further processing. In some embodiments, terminal 130 and display140 may be combined with image processing device 120 as single device.Similar modifications should fall within the scope of the presentdisclosure.

FIG. 2A illustrates an exemplary imaging device 110 according to someembodiments of the present disclosure. Imaging device 110 may obtain aprojection image of a projection object. The projection object may be anorganism, and an organ (e.g., a mammary gland, a hand, a head, a lung,etc.), or the like, or a combination thereof. The projection image maybe further processed by imaging processing device 120 (shown in FIG. 1and FIG. 3A) or computer 200 (shown in FIG. 2B). Imaging device 110 mayinclude a radiation source 201, a detector 203, and a compression plate202.

Radiation source 201 may emit radiation. The radiation may beelectromagnetic radiation (e.g., X-ray, gamma radiation, visible light,etc.), particle radiation (e.g., alpha radiation, beta radiation,neutron radiation, etc.), acoustic radiation (e.g., ultrasound),gravitational radiation, or the like, or a combination thereof. In someembodiments, radiation source 201 may be an X-ray source. In someembodiments, radiation source 201 may be configured as a bulb that mayemit X-radiation.

Radiation source 201 may include an even number (e.g., two, four, eight,sixteen, thirty, etc.) of sub-sources, or an odd number (e.g., one,three, five, thirty-one, etc.) of sub-sources. As used herein, asub-source (illustrated as 201-1 through 201-N in FIG. 2A) of radiationsource 201 may include a device or a structural component that may emitradiation. For instance, a sub-source may include a bulb that may emitradiation. In some embodiments, the number of sub-sources of radiationsource 201 may be one. In some embodiments, the number of sub-sources ofradiation source 201 may be more than one. At least two of a pluralityof sub-sources of radiation sources 201 may be the same or different intype (e.g., X-ray source, gamma radiation source, etc.). At least twosub-sources of radiation source 201 may have the same or differentcharacteristic parameter(s) (e.g., volume, shape, power, tube current,geometric magnification, total magnification, focus port size, radiationprotection, etc.).

Merely by way of example, radiation sub-sources 201-1 through 201-4 mayprovide X-ray radiation, and radiation sub-source 201-N may providegamma radiation. In some embodiments, the power of radiation sub-source201-1 may be 3 W, and the power of radiation sub-sources 201-2 through201-N may be 5 W. A source to image-receptor distance (SID) may be anylength (e.g., 0.5 m, 0.8 m, 1.0 m, 1.5 m, etc.). As used herein, SID mayrefer to a distance between radiation source 201 and a projectionimage-receptor (e.g., detector 203, etc.). If radiation source 201includes a plurality of sub-sources, SID may refer to a distance betweena sub-source of radiation source 201 and a projection image-receptor(e.g., detector 203, etc.). The SID of sub-sources 201-1 through 201-Nmay be the same or different.

In some embodiments, radiation sub-sources 201-1 through 201-N may bearranged in a straight line. The distances between two neighboringradiation sub-sources 201 may be the same or different. In someembodiments, all of radiation sub-sources 201-1 through 201-N may bearranged in a same line and the distances between each two neighboringradiation sub-sources (e.g., between radiation sub-source 201-1 andradiation sub-source 201-2, between radiation sub-source 201-2 andradiation sub-source 201-3, between radiation sub-source 201-3 andradiation sub-source 201-4, etc.) may be the same. In some embodiments,radiation sub-sources 201-1 through 201-N may be arranged in a curvedline, and at least two arc lengths between neighboring radiationsub-sources (e.g., between radiation sub-source 201-1 and radiationsub-source 201-2, radiation sub-source 201-2 and radiation sub-source201-3, etc.) are the same or different.

In some embodiments, radiation source 201 may be arranged in a whole ora part of circle with projection object (e.g., a mammary gland) at thecenter of the circle.

The location of one or more radiation sub-sources 201-1 through 201-Nmay be fixed or movable. In some embodiments, the location of one ormore radiation sub-sources 201-1 through 201-N may be fixed as describedabove. In some embodiments, the location of one or more radiationsub-sources 201-1 through 201-N may be changed according to theconfigurations of image reconstruction system 100. For example,radiation source 201 (or a radiation sub-source) may revolve around aprojection object to take one or more projection images. Radiationsource 201 (or a radiation sub-source) may revolve around a projectionobject in any angle range (e.g., −15° to +15°, −25° to +25°, −40° to+65°, −65° to +90°, etc.) when the vertical direction is denoted as 0°,a negative angle indicates an anti-clockwise rotation, and a positiveangle indicates a clockwise rotation. Radiation source 201 (or aradiation sub-source) may emit radiation at any fixed frequency of angle(e.g., in every 1°, in every 2°, in every 5°, and in every 10°, etc.).For example, radiation source 201 (or a radiation sub-source) may emitradiation at a fixed frequency of every 5° with an angle range −15° to+15° (i.e., at −15°, −10°, −5°, 0°, ++10°, +15°). Radiation source 201(or a radiation sub-source) may emit radiation at a variable frequencyof angle. For example, radiation source 201 (or a radiation sub-source)may emit radiation in 1°, 4°, 10°, 30°, and 90°.

Merely by way of example, radiation source 201 (or a radiationsub-source) may revolve around a projection object between −15° to +15°and emit radiation at every 1°. In that case, 31 projection images maybe generated. As another example, radiation source 201 (or a radiationsub-source) may revolve around projection object between −25° to +25°and emit radiation at every 2°. In that case, 26 projection images maybe generated.

Compression plate 202 and detector 203 and may hold the projectionobject from two opposite (or essentially opposite) directions.Compression plate 202 may be made of a rigid material. Compression plate202 may be flat or curved. In some embodiments, compression plate 202may be made of a material transparent to radiation (e.g., X-ray, etc.).Compression plate 202 may be parallel (or essentially parallel) todetector 203 (shown in FIG. 2A).

Detector 203 may measure the flux, spatial distribution, spectrum,and/or other properties of radiations. Radiation emitted by radiationsource 201 may pass through a projection object and reach detector 203to generate a projection image on detector 203. Detector 203 may be adirect semiconductor detector, a gas-filled detector, a scintillationdetector, or the like, or a combination thereof. Detector 203 may havean energy resolution including, for example, 125 eV, 145 eV, 165 eV, 180eV, 190 eV, 205 eV, 225 eV, etc. Detector 203 may have a detecting areaof, for example, 6 mm², 7 mm², 13 mm², 25 mm², etc. Detector 203 mayhave a thickness of, for example, 200 μm, 300 μm, 450 μm, 500 μm, 700μm, etc. Detector 203 may have a peaking time of, for example, 11.2 μs,32 μs, 44.8 μs, etc.

In some embodiments, a projection object may be a mammary gland. Aprojection image may be a projection image of the mammary gland.Radiation source 201 may be an X-ray source. Detector 203 may be anX-ray detector.

A projection image taken by imaging device 110 may be sent to imageprocessing device 120, data base 150, display 140, and/or terminal 130via network 160 shown in FIG. 1. In some embodiments, the projectionimage taken by imaging device 110 may be sent to image processing device120. Image processing device 120 may process the projection image. Forexample, image processing device 120 may generate a 3D reconstructedimage based on a plurality of projection images. In some embodiments,the projection image may be a projection image of a mammary gland. Imageprocessing device 120 may generate a 3D reconstructed image of a mammarygland based on a plurality of projection images of the mammary gland.The 3D mammary gland reconstructed image may include one or moretomographic images of a mammary gland.

It should be noted that the descriptions above in relation to imagingdevice 110 is provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, various variations and modificationsmay be conducted under the guidance of the present disclosure. However,those variations and modifications do not depart the scope of thepresent disclosure.

FIG. 2B illustrates an architecture of a computer 200 which may be usedto implement a specialized system incorporating the present teaching.Such a specialized system incorporating the present teaching has afunctional block diagram illustration of a hardware platform thatincludes user interface elements. Computer 200 may be a general purposecomputer or a special purpose computer. Both may be used to implement aspecialized system for the present teaching. Computer 200 may be used toimplement any component of image processing as described herein. Forexample, image processing device 120, etc. may be implemented on acomputer such as computer 200, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to imageprocessing as described herein may be implemented in a distributedfashion on a number of similar platforms, to distribute the processingload. In some embodiments, computer 200 may be used as imagingprocessing device 120 shown in FIG. 1.

Computer 200, for example, may include COM ports 211 connected to andfrom a network connected thereto to facilitate data communications.Computer 200 may also include a central processing unit (CPU) 205, inthe form of one or more processors, for executing program instructions.The exemplary computer platform may include an internal communicationbus 204, program storage, and data storage of different forms, e.g.,disk 208, read only memory (ROM) 206, or random access memory (RAM) 207,for various data files to be processed and/or communicated by thecomputer, as well as possibly program instructions to be executed by CPU205. Computer 200 may also include an I/O component 209, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 213. Computer 200 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of the image processing and/or otherprocesses, as described herein, may be embodied in programming. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Tangible non-transitory “storage” type media includeany or all of the memory or other storage for the computers, processors,or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of a scheduling system into thehardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with image processing. Thus, another type of media that maybear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s), or the like, which may be used to implement the system orany of its components shown in the drawings. Volatile storage media mayinclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media may include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media may include, forexample: a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD or DVD-ROM, any other opticalmedium, punch cards paper tape, any other physical storage medium withpatterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any othermemory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described herein maybe embodied in a hardware device, it may also be implemented as asoftware only solution—e.g., an installation on an existing server. Inaddition, image processing as disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

FIG. 3A illustrates an exemplary image processing device 120 accordingto some embodiments of the present disclosure. Image processing device120 may include an initialization module 310, a pre-procession module320, a reconstruction module 330, and an artifact removal module 340.Components in image processing device 120 may be connected to orcommunicate with each other and other components in image reconstructionsystem 100, for example, imaging device 110, terminal 130, display 140,database 150, or the like, or a combination thereof.

Initialization module 310 may initialize or adjust one or moreparameters relating to the configuration of image reconstruction system100. For example, the parameter(s) may be related to imaging device 110,image processing device 120, and terminal 130, or the like, or acombination thereof. The parameter(s) may be obtained from imagingdevice 110, image processing device 120, terminal 130, database 150, orthe like, or a combination thereof. The parameter(s) may be determinedbased on data obtained from imaging device 110, image processing device120, terminal 130, database 150, or the like, or a combination thereof.

The parameter(s) may include a coordinate of the radiation source, aradiation angle of the radiation source, the coordinate of a rotatingcenter of the radiation source, the pixel size of a projection image,the width of a projection image, the height of a projection image, thecoordinate vector of a pixel in a projection image, or the like, or acombination thereof.

In some embodiments, the parameter(s) may be a physical coordinate ofimage reconstruction system 100 (e.g., the coordinate of the radiationsource, a radiation angle of the radiation source, and the coordinate ofa rotating center of the radiation source, etc.), and an image parameterof a projection image (e.g., the pixel size of a projection image, thewidth of a projection image, and the height of a projection image,etc.).

Pre-procession module 320 may pre-process images. The images may beobtained by imaging device 110 or retrieved from another source (e.g., adatabase 150, a storage, etc.). Pre-procession module 320 may performone or more pre-processing operations on the image. The pre-processingoperations may include, for example, image segmentation, image negativefilm generation, geometrical error removal, color correction, geometrictransformation, image noise reduction, image enhancement, imagecompression, or the like, or a combination thereof.

As illustrated in FIG. 3B, pre-procession module 320 may include asegmentation unit 321, a negative film unit 323, and a geometrical errorcorrection unit 325. Segmentation unit 321 may segment a projectionimage to generate a segmented projection image. Negative film unit 323may generate a negative film of an image (e.g., a segmented projectionimage, etc.). Geometrical error correction unit 325 may correct ageometrical error of an image (e.g., a negative film of a segmentedprojection image, etc.).

Image segmentation may be performed based on, for example, an edgedetecting method, a threshold segmenting method, a histogram-basedmethod, a clustering method, a compression-based method, aregion-growing method, a graph partitioning method, or the like, or acombination thereof. The image segmentation operation may be performedby segmentation unit 321. In some embodiments, image segmentation may beperformed based on a region-growing method that may also be referred asa seed filling method. More descriptions about seed filling method maybe found elsewhere in the present disclosure. See, for example, FIG. 6and FIG. 7, and the description thereof.

In some embodiments, a projection image may be a projection image of amammary gland (or referred to as a mammary gland projection image). Themammary gland projection image may be pre-processed by one or morepre-processing operations including, for example, image segmentation,image negative film generation, geometrical error removal, or the like,or a combination thereof.

Pre-procession module 320 may generate a processed image. The processedimage may be sent to one or more other components in image processingdevice 120, for example, reconstruction module 330, artifact removalmodule 340, or the like, or a combination thereof. The processed imagemay be sent to one or more components in reconstruction system 100, forexample, terminal 130, display 140, database 150, or the like, or acombination thereof.

In some embodiments, pre-procession module 320 may pre-process a mammarygland projection image. The projection image may be obtained by imagingdevice 110 or retrieved from another source (e.g., a database 150, astorage, etc.). Pre-procession module 320 may generate a processedmammary gland projection image. The processed mammary gland projectionimage may be send to, for example, reconstruction module 330, artifactremoval module 340, or the like, or a combination thereof.

Reconstruction module 330 may perform image reconstruct. The image(s)subject to the reconstruction may be a projection image (e.g., a mammarygland projection image, etc.) or a processed projection image (e.g., aprocessed mammary gland projection image, etc.), or the like, or acombination thereof. The projection image may be generated by imagingdevice 110 or retrieved from another source (e.g., database 150, and astorage, etc.). The processed projection image may be generated bypre-procession module 320 or retrieved from another source (e.g.,database 150, and a storage, etc.). In some embodiments, the projectionimage may be generated by imaging device 110, and the processed imagemay be generated by pre-procession module 320. In some embodiments, theprojection image may be a mammary gland projection image obtained byimage device 110 and a processed image may be a processed mammary glandimage generated by pre-procession module 320.

As illustrated in FIG. 3C, the reconstruction module 330 may include afiltered a projection image generation unit 331, an artifact correctionunit 333, and a back projection unit 335. Filtered a projection imagegeneration unit 331 may generate a filtered projection image including ahighlighted artifact and an X-ray attenuation artifact. Artifactcorrection unit 333 may correct the highlighted artifact and the X-rayattenuation artifact in the filtered projection image to generate afirst image. Back projection unit 335 may perform back projection togenerate the reconstructed image based on the first image.

Reconstruction module 330 may generate a reconstructed image based onacquired images. The reconstructed image may include one or moretomographic images. The reconstructed image may be a 3D image. In someembodiments, the reconstructed image may be a 3D tomographic mammarygland reconstructed image. The reconstructed image generated byreconstruction module 330 may be sent to other component(s) in imageprocessing device 120, for example, pre-procession module 320, artifactremoval module 340, or the like, or a combination thereof. Thereconstructed image may be sent to one or more components inreconstruction system 100, for example, terminal 130, display 140,database 150, or the like, or a combination thereof.

Reconstruction module 330 may perform image reconstruction based on animage reconstruction method. The image reconstruction method may includefiltered back projection (FBP), a simultaneous iterative reconstructiontechnique (SIRT), matrix inversion tomosynthesis (MITS), iterativemaximum a posteriori statistical reconstruction, a Bayesian-basedinteractive reconstruction method, or the like, or a combinationthereof. In some embodiments, reconstruction module 330 may reconstructa mammary gland reconstructed image utilizing a filtered back projectionmethod. More descriptions regarding filtered back projection may befound elsewhere in the present disclosure. See, for example, FIG. 4 andFIG. 8, and the description thereof.

As illustrated in FIG. 3D, artifact removal module 340 may include adetector edge artifact removal unit 341, a mammary gland edge artifactremoval unit 343, and a serrated artifact removal unit 345. Detectoredge artifact removal unit 341, mammary gland edge artifact removal unit343, and serrated artifact removal unit 345 may be connected to orcommunicated with each other. Artifact removal module 340 may beconnected to or communicated with other component(s) in image processingdevice 120, for example, initialization module 310, pre-precessionmodule 320, or reconstruction module 330, or the like, or a combinationthereof. Artifact removal module 340 may be connected to or communicatedwith unit in reconstruction system 100, for example, terminal 130,display 140, database 150, or the like, or a combination thereof.

Artifact removal module 340 may remove artifact in a reconstructedimage. The reconstructed image may be generated by reconstruction module330 or retrieved from another source (e.g., database 150, and a storage,etc.). The reconstructed image may include one or more tomographicimages that may depict one or more layers of a projection object. Insome embodiments, the reconstructed image may be a mammary glandreconstructed image.

Artifact may be any error in a perception or representation in areconstructed image. Artifact may include detector edge artifact,mammary gland edge artifact, artifact caused by the movement of apatient, metal artifact, artifact caused by the arcing of a radiationsource (e.g., a bulb, etc.), artifact caused by a deviation of adetector from its normal operation condition, or the like, or acombination thereof. An artifact may have a regular shape (for example,streaking, ring, serration, etc.), or an irregular, or the like, or acombination thereof. In some embodiments, artifacts may include detectoredge artifact, mammary gland edge artifact, serrated artifact, or thelike, or a combination thereof.

Artifact removal module 340 may remove artifact utilizing variousartifact removing methods. The artifact removing method may include apolynomial interpolation method, an iterative deblurring method, anexpectation-maximization method, an algebraic reconstruction technique,a Markov random field method, a wavelet method, an ordered subsetsconvex iterative method, a beam-stop technique, a scanning lead-striptechnique, or the like, or a combination thereof.

Detector edge artifact removal unit 341 may remove detector edgeartifact. Detector edge artifact may have a strip shape, as shown inarea 1210 in FIG. 12. Detector edge artifact may be caused by a darkcurrent, a gain, a nonlinear error, a radiation damage, responsenonuniformity, detector afterglow, or the like, or a combinationthereof. Detector edge artifact may be removed by setting a gray valuein an area of detector edge artifact based on the gray value of pixelsin a neighborhood of the detector edge artifact. More descriptionsregarding removing detector edge artifact may be found elsewhere in thepresent disclosure. See, for example, FIG. 9 and the descriptionthereof.

A mammary gland tomographic image whose detector edge artifact have beenremoved by detector edge artifact removal unit 341 may still include aserrated artifact, as shown in area 1410 in FIG. 14. The serratedartifact may be removed by serrated artifact removal unit 345. In someembodiments, serrated artifact may be removed based on an intersectionpoint corresponding to a detector edge and a mammary gland edge, andcorresponding dislocation information. More descriptions regardingremoving serrated artifact may be found elsewhere in the presentdisclosure. See, for example, FIG. 10 and FIG. 11, and the descriptionthereof.

Mammary gland edge artifact removal unit 343 may remove mammary glandedge artifact. FIG. 21 illustrates a mammary gland reconstructed image.As shown in FIG. 21, there are mammary gland edge artifact in area 2110and area 2130. More descriptions regarding removing mammary gland edgeartifact may be found elsewhere in the present disclosure. See, forexample, FIG. 9 and the description thereof.

It should be noted that the descriptions above in relation to imageprocessing device 120 is provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, various variations and modificationsmay be conducted under the guidance of the present disclosure. However,those variations and modifications do not depart the scope of thepresent disclosure. For example, reconstruction module 330 may include afiltered back projection unit (no shown in figures) that may performfiltered back projection of a mammary gland projection image. As anotherexample, artifact removal module 340 may include one or more additionalartifact removal units (no shown in figures) that may remove one or moreother kinds of artifact (e.g., artifact caused by the movement of apatient, metal worn by a patient when the patient is scanned, the arcingof a bulb, and the deviation of a detector from its normal operationcondition, etc.). In some embodiments, the projection object may be anorganism, and an organ (e.g., a mammary gland, a hand, a head, a lung,etc.), or the like, or a combination thereof.

FIG. 4 illustrates a flowchart illustrating an exemplary process 400 forimage reconstruction in accordance with some embodiments of the presentdisclosure. In some embodiments, process 400 may be performed by one ormore devices (e.g., image processing device 120) in image reconstructionsystem 100 (shown in FIG. 1) and image processing device 120 (shown inFIG. 3A). In some embodiments, at least part of process 400 may beperformed by computer 200 shown in FIG. 2B.

In 410, one or more parameters may be initialized or adjusted. Theparameter initialization or adjustment in 410 may be performed byinitialization module 310 of FIG. 3A. The parameters may be related tothe configuration of image reconstruction system 100. For example, theparameters may be related to imaging device 110, image processing device120, and terminal 130, or the like, or a combination thereof.

The parameters may be obtained from imaging device 110, image processingdevice 120, terminal 130, database 150, or the like, or a combinationthereof. The parameters may be determined based on data obtained from,for example, imaging device 110, image processing device 120, terminal130, database 150, or the like, or a combination thereof. Detaileddescriptions about the parameters may be found elsewhere in the presentdisclosure. See, for example, FIG. 3A and the description thereof. Insome embodiments, the parameters may be a physical coordinate of imagereconstruction system 100 and an image parameter of a projection image.

In 420, one or more projection images may be obtained. The projectionimage(s) may be obtained by imaging device 110 or retrieved from anothersource (e.g., database 150, a storage, etc.). In some embodiments, theprojection image may be a mammary gland projection image.

In 430, the projection image may be a processed projection image thathas been subject to one or more pre-processing operations.Pre-processing in 430 may be performed by pre-procession module 320illustrated in FIG. 3A. A projection image may be pre-processedutilizing a pre-processing method including, for example, imagesegmentation, image negative film generation, geometrical error removal,color correction, geometric transformation, image noise reduction, imageenhancement, image compression, the like, or a combination thereof. Insome embodiments, a projection image may be a mammary gland projectionimage. More descriptions regarding methods of pre-processing aprojection image may be found elsewhere in the present disclosure. See,for example, FIG. 5 and the description thereof.

In 440, the processed projection image may be reconstructed to generatea reconstructed image. Image reconstruction in 440 may be performed byreconstruction module 330 of FIG. 3A. The reconstructed image mayinclude one or more tomographic images that may depict one or morelayers of a projection object. The processed projection image may bereconstructed utilizing a reconstruction method. Exemplaryreconstruction method may include filtered back projection, asimultaneous iterative reconstruction technique (SIRT), matrix inversiontomosynthesis (MITS), iterative maximum a posteriori statisticalreconstruction, a Bayesian-based interactive reconstruction method, orthe like, or a combination thereof.

Merely by way of example, a processed projection image may be aprocessed mammary gland projection image, and a reconstructed image maybe a mammary gland reconstructed image. The processed mammary glandprojection image may be reconstructed utilizing filtered backprojection. More descriptions regarding back projection reconstructionmethod may be found elsewhere in the present disclosure. See, forexample, FIG. 8 and the description thereof.

In 450, artifact in the reconstructed image may be removed. Artifactremoval in 450 may be performed by artifact removal module 340 of FIG.3A. The artifact may be take the form of any shape and/or type. Moredescriptions regarding artifact may be found elsewhere in the presentdisclosure. See, for example, FIG. 3A and the description thereof.

Artifact in a reconstructed image may be removed utilizing an artifactremoving method. The artifact removing method may include a polynomialinterpolation method, an iterative deblurring method, anexpectation-maximization method, an algebraic reconstruction technique,a Markov random field method, a wavelet method, an ordered subsetsconvex iterative method, a beam-stop technique, a scanning lead-striptechnique, or the like, or a combination thereof.

In some embodiments, a reconstructed image may be a mammary glandreconstructed image. Artifact in a reconstructed image may includedetector edge artifact, mammary gland edge artifact, serrated artifact,or the like, or a combination thereof. More descriptions regardingartifact removing method may be found elsewhere in the presentdisclosure. See, for example, FIG. 9 and the description thereof.

It should be noted that process 400 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protection scope of the presentdisclosure. In some embodiments, some steps may be reduced or added. Forexample, 430 may be omitted. A reconstructed image may be generatedbased on an original projection image without pre-processing. As anotherexample, 450 may be omitted. In some embodiments, the projection objectmay be an organism, an organ (e.g., a mammary gland, a hand, a head, alung, etc.), or the like, or a combination thereof. Similarmodifications should fall within the scope of the present disclosure.

FIG. 5 is a flowchart illustrating an exemplary process 500 forpre-processing projection image in accordance with some embodiments ofthe present disclosure. In some embodiments, process 500 may beperformed by pre-procession module 320 in imaging processing device 120(shown in FIG. 3A and FIG. 3B). In some embodiments, process 500described with reference to FIG. 5 may be an exemplary process forachieving 430 shown in FIG. 4.

In 510, a projection image may be segmented to obtain a segmentedprojection image including a region of interest. Image segmentation in510 may be performed by segmentation unit 321 of FIG. 3B. The projectionimage may be obtained by imaging device 110 or retrieved from anothersource (e.g., a database 150, a storage, etc.). The projection image maybe segmented utilizing an image segmentation method. The imagesegmentation method may include an edge detecting method, a thresholdsegmenting method, a histogram-based method, a clustering method, acompression-based method, a region-growing method, a graph partitioningmethod, or the like, or a combination thereof.

An edge detection method may be performed based on an edge detectionalgorithm. The edge detection algorithm may include, for example, theSobel edge detection algorithm, the Canny edge detection algorithm, aphase congruency-based algorithm, or the like, or a combination thereof.

A threshold segmenting method may be performed by classifying pixels inan image based on a fixed pixel value. For example, a pixel may bedeemed as a black pixel if its pixel value exceeds the fixed pixelvalue; a pixel may be deemed a white pixel if its pixel value is smallerthan the fixed pixel value.

A region-growing method may also be referred as a seed filling method. Aregion-growing method may be performed by selecting one or more seedsand determining whether one or more neighboring pixels of the selectedseeds may be added to the region.

A histogram-based method may be performed by determining a gray valuehistogram based on the gray value of pixels in an image. One or morepeaks and valleys in a histogram may be used to determine an edge of aregion of interest in the image.

In some embodiments, a projection image may be a mammary glandprojection image. The mammary gland projection image may be segmented toobtain a region of mammary gland. The mammary gland projection image maybe segmented utilizing the region-growing method that may be also bereferred to as the seed filling method. More descriptions regardingregion-growing method may be found elsewhere in the present disclosure.See, for example, FIG. 7 and the description thereof.

In 520, a segmented projection image may be processed to generate anegative film. Negative film operation in 520 may be performed bynegative film unit 323 of FIG. 3B. A negative film may be an image inwhich the darkness of a portion of the projection object reverselyrelates to the darkness of the same portion in the film or image. Forinstance, in a negative film, a lightest area of the projection objectappears darkest in the film, and a darkest area of the projection objectappears lightest in the film.

In some embodiments, 520 may include one or more of the followingoperations. A maximum gray value Max_A in a segmented projection imagemay be determined. A corrected gray value of each pixel in the segmentedprojection image may be determined by subtracting its gray value fromMax_A. The corrected gray value of a pixel may be assigned to the pixelas its gray value.

In 530, a geometrical error of the negative film of a segmentedprojection image may be corrected. Geometrical error correctionoperation in 530 may be performed by geometrical error correction unit325 of FIG. 3B. A geometrical error may include, for example, atranslation error of the detector, a rotation error of the detector, orthe like, or a combination thereof.

A translation error of the detector may be caused by the translation ofthe detector in a horizontal direction. As used herein, “horizontaldirection” may refer to a direction along the x-y plane shown in FIG.2A. In some embodiments, the translation error of the detector may beremoved by one or more of the following operations. A coordinate matrixof the pixels in a segmented projection image in a first coordinatesystem may be obtained. A translation vector of a pixel in the firstcoordinate system and in a second coordinate system may be determined.In some embodiments, the translation vector may be determined bysubtracting the coordinate of a pixel in the first coordinate systemfrom its coordinate in the second coordinate system. A correctedcoordinate matrix of pixels of the segmented projection image in thesecond coordinate system may be determined based on the coordinatematrix of the first coordinate system and the translation vector.

A rotation error of the detector may be caused by a rotation of thedetector about a vertical direction. As used herein, “verticaldirection” may refer to a direction along the z-axis shown in FIG. 2A.In some embodiments, a rotation error of the detector may be removed byone or more of the following operations. A mapping relationship betweena coordinate matrix of pixels in a segmented projection image in a firstcoordinate system and its coordinate system in a second coordinatesystem may be determined. A coordinate of each pixel in the secondcoordinate system may be determined based on the mapping relationship. Agray value of each pixel in the segmented projection image may bedetermined by utilizing an interpolation algorithm. The interpolationalgorithm may include an image interpolation algorithm, a bilinearinterpolation algorithm, a recent field interpolation algorithm, or thelike, or a combination thereof.

It should be noted that process 500 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, some steps may be reduced or added. Forexample, 520 may be omitted. As another example, 530 may be omitted. Insome embodiments, 510, 520, and 530 may be performed in any order. Forexample, 520 may be performed before 510. A projection image may beprocessed to generate a negative film first and then be segmented. As afurther example, 530 may be performed before 510 and 520. Similarmodifications should fall within the scope of the present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process 600 forsegmenting a projection image in accordance with some embodiments of thepresent disclosure. In some embodiments, process 600 may be performed bypre-procession module 320 in imaging processing device 120 (shown inFIG. 3A). In some embodiments, process 600 described with reference toFIG. 6 may be an exemplary process for achieving 510 shown in FIG. 5.

In 610, an average gray value of one or more pixels of a projectionimage may be determined. The projection image may be obtained by imagingdevice 110 or retrieved from another source (e.g., a database 150, astorage, etc.). In some embodiments, the projection image may be amammary gland projection image.

In 630, one of two marks, e.g., mark A or mark B, may be assigned to oneor more pixels of the projection image based on the relationship betweenthe gray value of a pixel and the average gray value. Merely by way ofexample, mark A may correspond to the value of 0, and mark B maycorrespond to the value of 1.

The relationship between the gray value of a pixel and the average grayvalue may be determined according to any rule. In some embodiments, therelationship may be determined according numerical values of the grayvalue of a pixel and the average gray value. For example, A may beassigned to a pixel when its gray value is less than the average grayvalue. B may be assigned to a pixel when its gray value is not less thanaverage gray value. As another example, A may be assigned to a pixelwhen the difference between its gray value and the average gray value isnot less than a first number, for example, 1, or 5, or 10, or the like.B may be assigned to a pixel when the difference between its gray valueand the average gray value is less than a second number, for example, 1,or 4, or 7, and the like. The first number may be the same as ordifferent from the second number.

In 650, the boundary of a region of interest may be determined based onthe marks of pixels in the projection image. A region of interest mayalso be referred to as a target area. In some embodiments, a region ofinterest may be a region of a mammary gland in a projection image. Theboundary of the region of interest may be determined utilizing an edgedetecting method, a threshold segmenting method, a histogram-basedmethod, a clustering method, a compression-based method, aregion-growing method, a graph partitioning method, or the like, or acombination thereof. More descriptions regarding region-growing methodmay be found elsewhere in the present disclosure. See, for example, FIG.7 and the description thereof.

In some embodiments, a segmented projection image may include a regionof interest determined by process 600. In some embodiments, a segmentedprojection image may include a segmented region based on a plurality ofregions of interest. The plurality of regions of interest may bedetermined by process 600, respectively, based on a plurality ofprojection images.

A segmented region may be determined by various ways. In someembodiments, the segmented region may be a union of a plurality ofregions of interest. As shown in FIG. 19, a1, a2, and a3 illustratethree regions of interest, and b illustrates the overlapping of a1, a2,and a3. A segmented region is region c, which is a union of a1, a2, anda3. In some embodiments, the segmented region may be determined based onthe coordinates of pixels in regions of interest. As shown in FIG. 20,the segmented region may be rectangle S whose diagonal vertexes may beM(X₁, Y₁) and N(X₂, Y₂). X₁ may be the largest horizontal coordinatevalue of all pixels of the plurality of regions of interest. Y₁ may bethe smallest longitudinal coordinate value of all pixels of theplurality of regions of interest. X₂ may be the smallest horizontalcoordinate value of all pixels of the plurality of regions of interest.Y₂ may be the largest longitudinal coordinate value of all pixels of theplurality of regions of interest. In some embodiments, X₂ may be 0 ifthe projection image is taken when a patient is standing. In someembodiments, Y₁ may be 0 if the projection image is taken when thepatient is lying. In some embodiments, X₁ may be the largest horizontalcoordinate value of all pixels of the plurality of regions of interest.Y₁ and X₂ may be 0. Y₂ may be the largest height of the plurality ofprojection images.

It should be noted that process 600 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, in 610, the median value or mode or anyother statistic of the gray values of one or more pixels of a projectionimage may be determined based on the gray value of the one or morepixels of the projection image. In 630, a mark may be assigned to apixel of a projection image based on the relationship between the grayvalue of the pixel and the median value or mode or any other statisticparameter of the gray values of one or more pixels of a projectionimage. In some embodiments, in 630, any number of marks may be assignedto a pixel of a projection image based on the relationship between thegray value of a pixel and the average gray value. For example, thenumber of marks that may be assigned to a pixel may be three, four,five, or the like, or a combination thereof. Similar modificationsshould fall within the scope of the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary process 700 fordetermining the boundary of a region of interest in accordance with someembodiments of the present disclosure. Process 700 may also be referredto as a region-growing method or a seed filling method. In someembodiments, process 700 may be performed by pre-procession module 320in imaging processing device 120. In some embodiments, process 700described with reference to FIG. 7 is an exemplary process for achieving650 shown in FIG. 6.

In 710, a seed pixel, or referred to as a seed, may be determined fromone or more pixels of a projection image. The seed pixel may be anypixel in the projection image. In some embodiments, the seed pixel maybe a pixel whose gray value is not less than the average gray value,which may be assigned mark B according to the example described above.In some embodiments, the seed pixel may be a pixel in the lower leftcorner or the upper left corner of a projection image and whose mark isB.

In 720, pixels in a M×N×Q neighborhood of the seed pixel may besearched. M, N, and Q may be a positive integer of any value. At leasttwo of M, N, and Q may be equal to each other, or different from eachother. In some embodiments, M, N, and P may equal to 3. Some or allpixels in the M×N×Q neighborhood of the seed pixel may be searched.Merely by way of example, 8 pixels may be searched in the M×N×Qneighborhood of the seed pixel. As another example, 4 pixels may besearched in the M×N×Q neighborhood of the seed pixel.

In 730, a judgment may be made as to whether there is a pixel marked asA in the M×N×Q neighborhood of the seed pixel. According to the examplealready described, a pixel may be assigned to mark A when its gray valueis less than the average gray value (see 630). If there is a pixel thatis assigned mark A in the M×N×Q neighborhood of the seed pixel, 740 maybe performed. Otherwise, 750 may be performed.

In 740, the pixel in the M×N×Q neighborhood of the seed pixel andassigned mark A may be recorded as a boundary pixel. The boundary pixelmay be located on the boundary of region of interest.

In 750, the pixel in M×N×Q neighborhood of seed pixel may be recorded asan internal pixel. The internal pixel may be located inside the regionof interest.

In 760, a judgment may be made as to whether there is a pixel that isassigned mark B in the M×N×Q neighborhood of the seed pixel and has notbeen recorded either as an internal pixel or a boundary pixel. Asdescribed above with reference to 630, a pixel is assigned mark B whenits gray value is not less than the average gray value. If there is apixel in the M×N×Q neighborhood of the seed pixel that is assigned markB and has not be recorded either as an internal pixel or a boundarypixel, 780 may be performed. Otherwise, 770 may be performed.

In 770, the search for a pixel in the M×N×Q neighborhood of the seedpixel may be finished.

In 780, the pixel in the M×N×Q neighborhood of the seed pixel that ismarked as B and has not be recorded may be designated as a seed pixel.The operations in 720 to 770 may be repeated until all pixels in theM×N×Q neighborhood of the seed pixel have been recorded and the searchmay terminate.

Process 700 may identify one or more boundary pixels in the projectionimage. The boundary of a region of interest may be determined byconnecting adjacent boundary pixels. In some embodiments, the boundaryof the region of interest may be a maximum boundary connecting adjacentboundary pixels. For instance, if there are more than one way ofconnecting two adjacent boundary pixels, the shortest connection may bedesignated as the section of boundary connecting the two adjacentboundary pixels. As another example, if there are more than one way ofconnecting two adjacent boundary pixels, the connection whose resultantregion of interest has a largest area may be designated as the sectionof boundary connecting the two adjacent boundary pixels.

In some embodiments, a projection image in process 700 may be a mammarygland projection image. A boundary of the mammary gland in a projectionimage may be determined by performing process 700.

It should be noted that process 700 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, any number of seed pixels (e.g., 3, 5,10, etc.) may be determined from one or more pixels of a projectionimage.

FIG. 8 is a flowchart illustrating an exemplary process 800 forgenerating a reconstructed image in accordance with some embodiments ofthe present disclosure. Process 800 may also be referred to as filteredback projection. In some embodiments, process 800 may be performed byreconstruction module 330 of imaging processing device 120 shown in FIG.3A and FIG. 3C. In some embodiments, process 800 described withreference to FIG. 8 may be an exemplary process for achieving 440 shownin FIG. 4.

In 810, a projection image from one or more projection angles may befiltered to generate a filtered projection image. Filtered projectionimage generation operation in 810 may be performed by filteredprojection image generation unit 331 of FIG. 3C. The projection imagemay be filtered according to a filter algorithm. The filter algorithmmay include the Ramp-Lak filter algorithm, the Shepp-Logan filteralgorithm, the Hamming filter algorithm, or the like, or a combinationthereof. In some embodiments, the filtered projection image may includeartifact such as a highlighted artifact, an X-ray attenuation artifact,a detector edge artifact, a mammary edge artifact, a serrated artifact,or the like, or a combination thereof.

In 830, the highlighted artifact in the filtered projection image may becorrected. Highlighted artifact correction operation in 830 may beperformed by artifact correction unit 333 of FIG. 3C. The highlightedartifact may take the form of a highlight edge around the projectionobject in a projection image. The highlighted artifact may be caused byfiltering.

In 850, an X-ray attenuation artifact in the filtered projection imagemay be corrected. X-ray attenuation artifact correction operation in 850may be performed by artifact correction unit 333 of FIG. 3C. The X-rayattenuation artifact may be caused by difference in activities betweenX-ray photons. As described with reference to FIG. 2, radiation emittedby radiation source 201 may pass through a projection object and reachdetector 203 to generate a projection image on detector 203. As X-raypasses through the projection object, low energy X-ray photons may beattenuated more, and the remaining high energy photons may be attenuatedless than low energy photons. Such a difference in photon attenuationmay cause X-ray attenuation artifact in the projection image.

In 870, a reconstructed image may be generated by performing a backprojection operation on the filtered projection image. Back projectionoperation in 870 may be performed by back projection unit 335 of FIG.3C. The reconstructed image may include one or more tomographic images.Back projection may be performed based on the inverse transformation ofeach view through a filtered projection image in the direction it wasoriginally acquired. As used herein, “view” may refer to an angle atwhich a projection image is obtained.

In some embodiments, the projection image in process 800 may be amammary gland projection image. The reconstructed image may be areconstructed mammary gland projection image. A plurality of mammarygland projection images may be processed based on the filtered backprojection operation to generate a reconstructed mammary glandprojection image.

It should be noted that process 800 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, some steps may be omitted or added. Forexample, 830 or 850 may be omitted. In some embodiments, 810, 830, and850 may be performed in any order. For example, 830 and 850 may beperformed at the same time. As another example, 850 may be performedbefore 830. As a further example, 830 and/or 850 may be performed before810. In some embodiments, the projection object may be an organism, andan organ (e.g., a mammary gland, a hand, a head, a lung, etc.), or thelike, or a combination thereof.

FIG. 9 is a flowchart illustrating an exemplary process 900 for removingartifact in a reconstructed image in accordance with some embodiments ofthe present disclosure. In some embodiments, process 900 may beperformed by artifact removal module 340 of imaging processing device120 shown in FIG. 3A and FIG. 3D. In some embodiments, process 900described with reference to FIG. 9 may be an exemplary process forachieving 450 shown in FIG. 4.

In 910, a detector edge artifact may be removed. The artifact removal in910 may be performed by detector edge artifact removal unit 341. Moredescriptions regarding a detector edge artifact may be found elsewherein the present disclosure. See, for example, FIG. 3A and the descriptionthereof.

The detector edge artifact may be removed by setting the gray values ofthe pixels in a detector edge artifact based on the gray values ofpixels in the neighborhood of the area of a detector edge artifact. Insome embodiments, the process for removing a detector edge error mayinclude one or more of the following operations. The neighborhood areaof a detector edge artifact may be determined. The neighborhood area maybe an area close to the detector edge artifact and outside of thedetector edge artifact. The neighborhood area may be an area of any sizeor shape. The average gray value of the pixels in the neighborhood areamay be determined. In some embodiments, the pixels in the detector edgeartifact in the tomographic image (e.g., a same slice of a CT image,etc.) may be assigned a same gray value. For instance, the gray valuesof the pixels in the detector edge artifact may be assigned the averagegray value of pixels in the neighborhood area.

In 930, a projection object edge artifact may be removed. In someembodiments, the projection object may include a mammary gland. In someembodiments, the projection object edge may include the edge of themammary gland. In some embodiments, the process for removing a mammarygland edge artifact may include one or more of the following operations.The boundary of the projection object in one or more projection imagesfrom one or more views may be determined by an edge detection algorithm.The edge detection algorithm may include, for example, the Sobel edgedetection algorithm, the Canny edge detection algorithm, a phasecongruency-based algorithm, the Otsu's algorithm, or the like, or acombination thereof. For example, the boundary of a projection objectmay be detected by the Otsu's algorithm first and then by the Sobel edgedetection algorithm. A 3D projection object surface may be generatedbased on one or more projection images from one or more views using asimultaneous algebraic reconstruction technique (SART). The pixel valuedistribution of each projection image from a projection view may beupdated based on the boundary of a projection image. The gray value ofthe pixels outside of the region of the projection object may be set as0 after each iteration in SART. A pixel may be determined to be outsideof the region of the projection object based on the 3D projection objectsurface.

In some embodiments, the projection object may be a mammary gland. Theartifact removal in 930 may be performed by mammary gland edge artifactremoval unit 343.

In 950, a serrated artifact may be removed. The artifact removal in 950may be performed by serrated artifact removal unit 345 shown in FIG. 3A.More descriptions regarding a serrated artifact removal method may befound elsewhere in the present disclosure. See, for example, FIG. 10 andFIG. 11, and the description thereof.

It should be noted that process 900 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, some steps may be reduced or added. Forexample, 930 may be omitted. In some embodiments, 930 and 910 may beperformed at the same time. In some embodiments, 930 may be performedbefore 910. In some embodiments, one or more steps may be added toremove one or more other artifacts including, for example, an artifactcaused by the movement of the patient, metal worn by the patient whenthe patient is scanned, the arcing of the radiation source (e.g., abulb, etc.), a deviation of the detector from its normal operationcondition, or the like, or a combination thereof. In some embodiments,the projection object may be an organism, and an organ (e.g., a mammarygland, a hand, a head, a lung, etc.), or the like, or a combinationthereof.

FIG. 10 is a flowchart illustrating an exemplary process 1000 forremoving serrated artifact in accordance with some embodiments of thepresent disclosure. In some embodiments, process 1000 may be performedby serrated artifact removal unit 345 of imaging processing device 120shown in FIG. 3A. In some embodiments, process 1000 described withreference to FIG. 10 may be an exemplary process for achieving 950 asshown in FIG. 9.

In some embodiments, a serrated artifact may be present in areconstructed image. FIG. 13 illustrates a mammary gland reconstructedimage. As shown in FIG. 13, serrated artifacts are present in region1310. FIG. 14 illustrates a mammary gland reconstructed image withoutserrated artifact correction. As shown in FIG. 14, serrated artifactsare present in area 1410 and 1420. The mammary gland reconstructed imageshown in FIG. 15A and FIG. 15B includes serrated artifact S (e.g., S1,S2, S3, S4, S1′, S2′, S3′, S4′).

In 1010, the mapping position of the detector edge in a projection imagefrom a projection view with respect to the detector edge in acorresponding tomographic image may be determined. A tomographic imagemay be part of a reconstructed image. The reconstructed image mayinclude one or more tomographic images. A tomographic image may depict alayer of the projection object. In some embodiment, a tomographic imagemay be a mammary gland tomographic image that may depict a layer of themammary gland. FIG. 15A depicts an upper portion of a mammary gland andFIG. 15B depicts a lower portion of the mammary gland. Horizontal dottedlines L1-L4 and L1′-L4′ depict mapping positions of the detector edge.

In some embodiments, 1010 may include one or more of the followingoperations. A first geometric position relationship between radiationsource 201 and detector 203 (shown in FIG. 2A) may be determined. Asecond geometric position relationship between a projection image from aprojection view and a corresponding tomographic image may be determined.The mapping coordinates of a pixel in the projection image with respectto the corresponding pixel in the corresponding tomographic image may bedetermined based on the first geometric position relationship and thesecond geometric position relationship. As used herein, a pixel in aprojection image and the corresponding pixel in a correspondingtomographic image may relate to a same portion (e.g., a same spot, etc.)of the projection object. The mapping coordinates of a pixel in theprojection image with respect to the corresponding pixel in thetomographic image may be determined utilizing, for example, an imageinterpolation algorithm, a bilinear interpolation algorithm, a recentfield interpolation algorithm, or the like, or a combination thereof.According to a bilinear interpolation algorithm, the mapping coordinatesof a pixel may be determined based on the coordinates of two neighboringpixels. In a recent field interpolation algorithm, the mappingcoordinates of a pixel may be determined based on the coordinates of aneighboring pixel closest to the pixel. The mapping position of thedetector edge in the projection image with respect to the detector edgein the corresponding tomographic image may be determined based on animaging area of the detector in the projection image and the mappingcoordinates of the pixels of the detector edge in the projection image.

For example, the resolution of a projection image may be 1000*1000. Themapping coordinates of a pixel in the projection image with respect tothe corresponding pixel in the corresponding tomographic image may besmaller than 0 or larger than 1000. A pixel whose mapping coordinatesare smaller than 0 or larger than 1000 may be a pixel outside of theimaging area of the detector. The detector edge may be determined by acritical value (e.g., 0 and 1000). As shown in FIG. 15A and FIG. 15B,line L (e.g., L1, L2, L3, L4, L1′, L2′, L3′ and L4′) includes themapping position corresponding to the detector edge. In someembodiments, a point (x, y) on the line L may be described in atwo-dimensional array. For example, (1, 2) may describe a point with ahorizontal coordinate of 1 and a vertical coordinate of 2 on the line L.

In 1020, a projection object edge in the tomographic image may bedetermined. The projection object edge may be a boundary between an areaof projection object and a direct exposing area. As used herein, adirect exposing area may refer to an area of air (e.g., an area outsideof a projection object, etc.).

In some embodiments, a projection object may be a mammary gland. Themammary gland edge in a tomographic image may be the boundary betweenregion of the mammary gland and a region outside of the mammary gland(i.e., a direct exposing area). For example, as shown in FIG. 15A, thegray area may depict a region corresponding to a mammary gland (i.e., aregion of mammary gland), and the dark area may depict the regionoutside of the mammary gland (i.e., a direct exposing area). There is aboundary with a serrated line between the gray area and the dark area.The mammary gland edge may be the boundary between the gray area and thedark area.

In 1030, an intersection point corresponding to the projection objectedge and the mapping position corresponding to the detector edge may bedetermined.

As shown in FIG. 15A, Point P (e.g., P1, P2, P3, and P4) is anintersection point corresponding to the mammary gland edge (the boundarybetween the gray area and the dark area) and the mapping positioncorresponding to the detector edge (the horizontal dotted lines L1, L2,L3, and L4). Artifact S (e.g., S1, S2, S3, and S4) has a shape ofserrations, and referred to as a serrated artifact. Point P1, P2, P3,and P4 may be roughly horizontal to serrated artifact S1, S2, S3, andS4.

In 1040, dislocation information of each intersection point may bedetermined based on the intersection point and the mapping position ofthe detector edge. The dislocation information may be a distance betweenthe intersection point and an edge point of a corresponding serratedartifact. The corresponding serrated artifact may be the serratedartifact that is roughly horizontal to the intersection point.

For example, in FIG. 15A, the corresponding serrated artifact ofintersection point P1 may be serrated artifact S1. Dislocationinformation of the intersection point P1 may be the distance between theintersection point P1 and the edge point of the serrated artifact S1,which is denoted as D1.

In 1050, the serrated artifact may be removed based on the intersectionpoint and the dislocation information. The serrated artifact may beremoved by moving its edge for a distance towards the region of theprojection object (e.g., a mammary gland, etc.).

In some embodiments, the distance may be equal to the dislocationinformation of the intersection point. In some embodiments, the distancemay be a statistic value determined based on the dislocation informationof a plurality of intersection points. For example, the distance may bethe average value of the dislocation information of one or moreintersection points in the reconstructed image. As another example, thedistance may be the median value of the dislocation information of oneor more intersection points in the reconstructed image.

In some embodiments, as shown in FIG. 15A, serrated artifact S1 may beremoved by moving its edge toward the region of the mammary gland (thegray area) for a distance equal to the dislocation information ofintersection point P1. More descriptions regarding the method to removeserrated artifact based on intersection point and the correspondingdislocation information may be found elsewhere in the presentdisclosure. See, for example, FIG. 11 and the description thereof.

It should be noted that process 1000 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, 1010 and 1020 may be performed at thesame time. In some embodiments, 1020 may be performed before 1010. Insome embodiments, the projection object may be an organism, and an organ(e.g., a mammary gland, a hand, a head, a lung, etc.), or the like, or acombination thereof.

FIG. 11 is a flowchart illustrating an exemplary process 1100 forremoving a serrated artifact based on an intersection point and relevantdislocation information in accordance with some embodiments of thepresent disclosure. In some embodiments, process 1100 may be performedby serrated artifact removal unit 345 in imaging processing device 120as shown in FIG. 3A. In some embodiments, process 1100 described withreference to FIG. 11 may be an exemplary process for achieving 1050 asshown in FIG. 10.

In 1110, a projection object template of a tomographic image may begenerated. The projection object template may reduce image processingcost. The projection object template may be generated by setting grayvalues of pixels in a number of different regions of the tomographicimage. In some embodiments, the tomographic image may have two differentregions (e.g., a projection object region and a direct exposing region)and a binary projection object template may be used. For example, thegray value of pixels in the projection object region may be set to E,and the gray value of pixels out of the direct exposing region may beset to F. For example, E may be 1 and F may be 0. In some embodiments,the tomographic image may have more than two different regions and amulti-value projection object template may be used. For example, thetomographic image may have three different regions (e.g., a soft tissueregion, a bone region, and a direct exposing region) and a three-valueprojection object template may be used. The number of different regionsmay be any integer (e.g., one, two, three, four, etc.).

In some embodiments, a projection object may be a mammary gland and amammary gland template may be used. As shown in FIG. 16A, the grayvalues of the pixels in a region of the mammary gland may be set as 1,and the gray values of the pixels in a direct exposing region may be setas 0. In that way, the region of the mammary gland may be depicted aswhite, and the direct exposing region may be depicted as black.

In 1130, a serrated artifact in the projection object template may beremoved to obtain a corrected projection object template without theserrated artifact. The serrated artifact may be removed in any order. Insome embodiments, the serrated artifact in the portion of the templatecorresponding to the region of a projection object close to the centerof the projection object may be removed first and the serrated artifactin the portion of the template corresponding to the region of theprojection object close to an edge of the projection object may beremoved afterwards. In some embodiments, the serrated artifact in theportion of the template corresponding to the region of a projectionobject close to the edge of the projection object may be removed firstand the serrated artifact in the portion of the template correspondingto the region of a projection object close to the center of theprojection object may be removed afterwards.

In some embodiments, a projection object may be a mammary gland and amammary gland template may be used. As shown in FIG. 16A and FIG. 16B, S(e.g., S1, S2, S3, S4, S1′, S2′, S3′ and S4′) may be a serrated artifactand E (e.g., E1, E2, E3 and E4) may be an edge of the serrated artifact.L (e.g., L1, L2, L3 and L4) may be the mapping position corresponding tothe detector edge. P (e.g., P1, P2, P3 and P4) may be an intersectionpoint corresponding to a mammary gland edge (e.g., a boundary betweenthe gray area and the dark area). D (e.g., D1, D2, D3 and D4) may bedislocation information of the corresponding intersection point.

Serrated artifact S1 to S4 in FIG. 16A may be removed in any order(e.g., in a successive order from S1 to S4, in a reversed order from S4to S1, etc.). Merely by way of example, serrated artifact S1 may beremoved first and then S2, S3, and S4 may be removed successively.Serrated artifact S1 may be removed by moving its edge E1 towards theportion of the image corresponding to the center of mammary gland thatis on the left side of E1 in FIG. 16A for distance D1. The moved edge ofserrated artifact is E1′ shown in FIG. 16B. The gray value of the pixelsin the area between E1 and E1′ may be set as 0 so that the area betweenE1 and E1′ may be black (See FIG. 16C). In that way, serrated artifactS1 may be removed to obtain a corrected mammary gland edge withoutserrated artifact S1.

A boundary between the light area and the dark area in FIG. 16C maydepict a corrected mammary gland edge after removing serrated artifactS1. The determination of an intersection point between the correctedmammary gland edge and the mapping position corresponding to thedetector edge may be repeated. As shown in FIG. 16D, P2′ is anintersection point corresponding to a corrected mammary gland edge and amapping position corresponding to detector edge L2. Serrated artifact S2to S4 may be successively removed using the same way of removingserrated artifact S1.

In 1150, a corrected tomographic image may be generated based on thetomographic image and the corrected projection object template in whichthe serrated artifact is removed (or referred to as without the serratedartifact). According to the corrected projection object template, acorrected region outside of the projection object (a dark area in thecorrected projection object template) may be obtained. The gray value ofthe pixels in the corresponding region outside of the projectionobjection in a tomographic image may be set as 0. In that way, theserrated artifact in the tomographic image may be removed to generate acorrected tomographic image. A projection object edge may be smooth oressentially smooth in the corrected tomographic image.

In some embodiments, the projection object may be a mammary gland. Asshown in FIG. 17, there are serrated artifacts in mammary gland edge inarea 1710 before serrated artifacts are removed, and there is no visibleserrated artifact along the mammary gland edge in area 1730 afterserrated artifacts are removed. In some embodiments, the projectionobject may be an organism, and an organ (e.g., a mammary gland, a hand,a head, a lung, etc.), or the like, or a combination thereof.

EXAMPLES

The following examples are provided for illustration purposes, and notintended to limit the scope of the present disclosure.

Example 1

FIG. 12 illustrates a reconstructed image of a mammary gland. As shownin FIG. 12, there are detector edge artifacts in region 1210. Thedetector edge artifacts are strip-shaped. The existence of the detectoredge artifacts may influence the results of a diagnosis. In someembodiments, the detector edge artifacts may be removed according toprocess 900 described with reference to FIG. 9.

Example 2

FIG. 13 illustrates an exemplary reconstructed image of a mammary gland.As shown in FIG. 13, there are serrated artifacts in region 1310. Theexistence of the serrated artifacts may influence the result of adiagnosis. In some embodiments, the serrated artifacts may be removedaccording to process 1000 and process 1100 described with reference toFIG. 10 and FIG. 11.

Example 3

FIG. 14 illustrates an exemplary reconstructed image of mammary glandwithout serrated artifact correction. As shown in FIG. 14, the topportion of FIG. 14 depicts an upper portion of a mammary gland, and thebottom portion of FIG. 14 depicts a lower portion of the mammary gland.There are serrated artifacts in region 1410 (at the upper edge of themammary gland reconstructed image) and region 1420 (at the bottom edgeof the mammary gland reconstructed image).

Example 4

FIG. 15A and FIG. 15B illustrate reconstructed images of a mammary glandwith serrated artifacts. FIG. 15A depicts an upper portion of a mammarygland. FIG. 15B depicts a lower portion of the mammary gland. Serratedartifact (e.g., S1, S2, S3, S4, S1′, S2′, S3′ and S4′) isserration-shaped. Line L (e.g., L1, L2, L3, L4, L1′, L2′, L3′ and L4′)is a mapping position corresponding to the detector edge. Point P (e.g.,P1, P2, P3, P4, P1′, P2′, P3′ and P4′) is an intersection pointcorresponding to a mammary gland edge (a boundary between the gray areaand the dark area) and line L. D (e.g., D1, D2, D3, D4, D1′, D2′, D3′and D4′) is dislocation information of intersection point P, which isthe distance between intersection point P and the edge point of acorresponding serrated artifact.

Example 5

FIG. 16A to FIG. 16D illustrate exemplary mammary gland templates. FIG.16A and FIG. 16B illustrate mammary gland templates before serratedartifact were removed. FIG. 16C and FIG. 16D illustrate mammary glandtemplates after serrated artifact S1 were removed. As shown in FIGS.16A-16D, line L (e.g., L1, L2, L3, and L4) is a mapping positioncorresponding to the detector edge. P (e.g., P1, P2, P3, P4, and P2′) isan intersection point corresponding to a mammary gland edge (a boundarybetween the gray area and the dark area) and line L. D (e.g., D1) isdislocation information of intersection point P, which is a distancebetween intersection point P and edge point of corresponding serratedartifact. E (e.g., E1, E2, E3, and E4) is an edge of the serratedartifact. E′ (e.g., E1) is a corrected artifact edge which was obtainedby moving edge E left for the distance equal to dislocation informationof the corresponding intersection point P. For example, E1′ was obtainedby moving E1 left for the distance of D1. P′ (e.g., P2′) is anintersection point of line L and a corrected mammary gland edge afterserrated artifact was removed.

Example 6

FIG. 17 illustrates exemplary mammary gland reconstructed images. Theleft portion of FIG. 17 was generated before serrated artifacts wereremoved. The right portion of FIG. 17 was generated after serratedartifacts were removed. As shown in FIG. 17, there are serratedartifacts along the mammary gland edge in area 1710 before serratedartifacts were removed, and there is no visible serrated artifact in themammary gland edge in area 1730 after serrated artifacts were removed.

Example 7

FIG. 18 illustrates an exemplary projection image of a mammary gland.FIG. 18 may be generated by imaging device 110 according to someembodiments of the present disclosure. As shown in FIG. 18, the mammarygland in area 1810 has a higher gray value than the right portion of theprojection image. The right portion of the projection image denoted asarea 1820 is the background with a lower gray value than the leftportion of the projection image.

Example 8

FIG. 19 illustrates a process for generating a segmented region bymerging a plurality of regions of interest according to some embodimentsof the present disclosure. As shown in FIG. 19, a1, a2, and a3 are threeregions of interest, and b is a region generated by overlaying a1, a2,and a3. C is a segmented region, which is a union of a1, a2, and a3.

Example 9

FIG. 20 illustrates a process for generating a segmented region based ona rectangular segmenting algorithm according to some embodiments of thepresent disclosure. As shown in FIG. 19, the gray area is a region of amammary gland that is a region of interest. The segmented region may berectangle S whose diagonal vertexes are M (X₁, Y₁) and N (X₂, Y₂). X₁ isthe largest horizontal ordinate value of all pixels in a plurality ofregions of interest. Y₁ is the smallest longitudinal ordinate value ofall pixels in the plurality of regions of interest. X₂ is the smallesthorizontal ordinate value of all pixels in the plurality of regions ofinterest. Y₁ is the largest longitudinal ordinate value of all pixels inthe plurality of regions of interest.

Example 10

FIG. 21 illustrates an exemplary reconstructed image of a mammary gland.As shown in FIG. 21, there are mammary gland edge artifacts in area 2110and area 2130.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the operator's computer, partly on the operator's computer,as a stand-alone software package, partly on the operator's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe operator's computer through any type of network, including a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method comprising: obtaining a projection image of aprojection object; pre-processing, by a processor, the projection imageacquired by an imaging device to generate a processed projection image,the imaging device including a radiation source and a detector;reconstructing, by the processor, the processed projection image togenerate a reconstructed image including an artifact; and removing, bythe processor, the artifact in the reconstructed image, thepre-processing the projection image including segmenting the projectionimage to generate a segmented projection image, the segmented projectionimage including a region of interest, and the segmenting the projectionimage including determining an average gray value of one or more pixelsof the projection image; for each pixel of the one or more pixels of theprojection image, assigning mark A or mark B to the pixel based on arelationship between a gray value of the pixel and the average grayvalue; and determining a boundary of the region of interest based on theassigned mark of each pixel of the one or more pixels of the projectionimage, the reconstructing the processed projection image to generate thereconstructed image including filtering the processed projection imageto generate a filtered projection image including a highlighted artifactand an X-ray attenuation artifact; correcting the highlighted artifactand the X-ray attenuation artifact in the filtered projection image togenerate a first image; and performing back projection to generate thereconstructed image based on the first image.
 2. The method of claim 1,the pre-processing the projection image further including: generating anegative film of the segmented projection image; and correcting ageometrical error of the negative film of the segmented projectionimage.
 3. The method of claim 1, the artifact including a detector edgeartifact relating to a detector edge of the detector, a projectionobject edge artifact relating to a projection object edge, and aserrated artifact, and the removing the artifact in the reconstructedimage including: removing the detector edge artifact; removing theprojection object edge artifact; and removing the serrated artifact. 4.The method of claim 3, the reconstructed image including a tomographicimage, and the removing serrated artifact including: determining amapping position of the detector edge in the tomographic image;determining the projection object edge in the tomographic image;determining an intersection point corresponding to the projection objectedge and the mapping position of the detector edge; determiningdislocation information of the intersection point based on theintersection point and the serrated artifact; and removing the serratedartifact based on the intersection point and the dislocation informationof the intersection point.
 5. The method of claim 4, the determining amapping position of the detector edge in the tomographic imageincluding: determining a first geometric position relationship betweenthe radiation source and the detector; determining a second geometricposition relationship between the projection image and the tomographicimage; determining mapping coordinates of pixels in the projection imagebased on the first geometric position relationship and the secondgeometric position relationship; and determining the mapping position ofthe detector edge based on the mapping coordinates of pixels in theprojection image and an imaging area of the detector in the projectionimage.
 6. The method of claim 4, the dislocation information of theintersection point is a horizontal distance between the intersectionpoint and a point on an edge of the serrated artifact.
 7. The method ofclaim 4, the removing the serrated artifact based on the intersectionpoint and the dislocation information of the intersection pointincluding: creating a projection object template of the tomographicimage; removing the serrated artifact in the projection object templateto obtain a corrected projection object template; and removing theserrated artifact in the tomographic image based on the correctedprojection object template.
 8. The method of claim 1, the boundary ofthe region of interest is determined based on a seed filling algorithm.9. A non-transitory computer readable medium comprising executableinstructions that, when executed by at least one processor, cause the atleast one processor to effectuate a method comprising: obtaining aprojection image of a projection object; pre-processing the projectionimage to generate a processed projection image; reconstructing theprocessed projection image to generate a reconstructed image includingan artifact; and removing the artifact in the reconstructed image, thepre-processing the projection image including segmenting the projectionimage to generate a segmented projection image, the segmented projectionimage including a region of interest, and the segmenting the projectionimage including determining an average gray value of one or more pixelsof the projection image; for each pixel of the one or more pixels of theprojection image, assigning mark A or mark B to the pixel based on arelationship between a gray value of the pixel and the average grayvalue; and determining a boundary of the region of interest based on theassigned mark of each pixel of the one or more pixels of the projectionimage, and the reconstructing the processed projection image to generatethe reconstructed image including filtering the processed projectionimage to generate a filtered projection image including a highlightedartifact and an X-ray attenuation artifact; correcting the highlightedartifact and the X-ray attenuation artifact in the filtered projectionimage to generate a first image; and performing back projection togenerate the reconstructed image based on the first image.
 10. A systemcomprising: at least one storage medium including a set of instructionsfor processing a projection image; at least one processor incommunication with the at least one storage medium, wherein whenexecuting the set of instructions, the at least one processor isconfigured to cause the system to: pre-process a projection imageacquired by an imaging device to generate a processed projection image,the imaging device including a radiation source and a detector;reconstruct the processed projection image to generate a reconstructedimage including an artifact; and remove the artifact, wherein thepre-processing of the projection image includes segmenting theprojection image to generate a segmented projection image, the segmentedprojection image including a region of interest, and the segmenting theprojection image includes determining an average gray value of one ormore pixels of the projection image; for each pixel of the one or morepixels of the projection image, assigning mark A or mark B to the pixelbased on a relationship between a gray value of the pixel and theaverage gray value; and determining a boundary of the region of interestbased on the assigned mark of each pixel of the one or more pixels ofthe projection image, the reconstructing the processed projection imageto generate a reconstructed image includes generating a filteredprojection image including a highlighted artifact and an X-rayattenuation artifact; correcting the highlighted artifact and the X-rayattenuation artifact in the filtered projection image to generate afirst image; and generating the reconstructed image based on the firstimage.
 11. The system of claim 10, the at least one processor is furtherconfigured to cause the system to generate a negative film of thesegmented projection image; and correct a geometrical error of thenegative film of the segmented projection image.
 12. The system of claim10, the reconstructed image including a tomographic image, thetomographic image including a serrated artifact, and the removingserrated artifact including: determining a mapping position of thedetector edge in the tomographic image; determining the projectionobject edge in the tomographic image; determining an intersection pointcorresponding to the projection object edge and the mapping position ofthe detector edge; determining dislocation information of theintersection point based on the intersection point and the serratedartifact; and removing the serrated artifact based on the intersectionpoint and the dislocation information of the intersection point.
 13. Thesystem of claim 12, the determining a mapping position of a detectoredge in the tomographic image including: determining a first geometricposition relationship between the radiation source and the detector;determining a second geometric position relationship between theprojection image and the tomographic image; determining mappingcoordinates of pixels in the projection image based on the firstgeometric position relationship and the second geometric positionrelationship; and determining the mapping position of the detector edgebased on the mapping coordinates of pixels in the projection image andan imaging area of the detector in the projection image.
 14. The systemof claim 12, wherein the dislocation information of the intersectionpoint is a horizontal distance between the intersection point and apoint on an edge of the serrated artifact.
 15. The system of claim 12,the removing the serrated artifact based on the intersection point andthe dislocation information of the intersection point including:creating a projection object template of the tomographic image; removingthe serrated artifact in the projection object template to obtain acorrected projection object template; and removing the serrated artifactin the tomographic image based on the corrected projection objecttemplate.
 16. The system of claim 10, the artifact including a detectoredge artifact relating to a detector edge of the detector, a projectionobject edge artifact relating to a projection object edge, and aserrated artifact.
 17. The system of claim 16, wherein the at least oneprocessor is further configured to cause the system to remove thedetector edge artifact, the projection object edge artifact, and theserrated artifact.